
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase				=			logging.get_logger(__name__)

__lowerCamelCase				=			{
    """s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}






class     UpperCAmelCase						(		__snake_case					):
   A__   :						Union[str, Any]			=	'open-llama'



   def __init__(self						:   List[str]       ,		snake_case__						:   Optional[int]=10_00_00       ,		snake_case__						:   List[str]=40_96       ,		snake_case__						:   Optional[Any]=1_10_08       ,		snake_case__						:   Tuple=32       ,		snake_case__						:   int=32       ,		snake_case__						:   Dict="silu"       ,		snake_case__						:   Tuple=20_48       ,		snake_case__						:   Union[str, Any]=0.02       ,		snake_case__						:   Tuple=1e-6       ,		snake_case__						:   Dict=True       ,		snake_case__						:   List[str]=0       ,		snake_case__						:   List[str]=1       ,		snake_case__						:   Union[str, Any]=2       ,		snake_case__						:   Dict=False       ,		snake_case__						:   Dict=True       ,		snake_case__						:   Optional[int]=0.1       ,		snake_case__						:   Union[str, Any]=0.1       ,		snake_case__						:   Dict=True       ,		snake_case__						:   Union[str, Any]=True       ,		snake_case__						:   str=None       ,		**snake_case__						:   Optional[int]       ,		)						->      str:


          '''simple docstring'''


          snake_case			:  Tuple     							= vocab_size
          snake_case			:  List[str]     							= max_position_embeddings
          snake_case			:  Optional[int]     							= hidden_size
          snake_case			:  Dict     							= intermediate_size
          snake_case			:  Any     							= num_hidden_layers
          snake_case			:  int     							= num_attention_heads
          snake_case			:  str     							= hidden_act
          snake_case			:  List[str]     							= initializer_range
          snake_case			:  int     							= rms_norm_eps
          snake_case			:  str     							= use_cache
          snake_case			:  Dict     							= kwargs.pop(
              "use_memorry_efficient_attention"       ,		snake_case__					)
          snake_case			:  Optional[int]     							= hidden_dropout_prob
          snake_case			:  int     							= attention_dropout_prob
          snake_case			:  int     							= use_stable_embedding
          snake_case			:  Union[str, Any]     							= shared_input_output_embedding
          snake_case			:  Union[str, Any]     							= rope_scaling
          self._rope_scaling_validation()

          super().__init__(
              pad_token_id=snake_case__       ,		bos_token_id=snake_case__       ,		eos_token_id=snake_case__       ,		tie_word_embeddings=snake_case__       ,		**snake_case__       ,		)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any					)						->      Dict:


          '''simple docstring'''


          if self.rope_scaling is None:
                 return

          if not isinstance(self.rope_scaling       ,		snake_case__					) or len(self.rope_scaling					) != 2:
                 raise ValueError(
                     "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
                     f"""got {self.rope_scaling}"""					)
          snake_case			:  Any     							= self.rope_scaling.get("type"       ,		snake_case__					)
          snake_case			:  List[Any]     							= self.rope_scaling.get("factor"       ,		snake_case__					)
          if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
                 raise ValueError(
                     f"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""					)
          if rope_scaling_factor is None or not isinstance(snake_case__       ,		snake_case__					) or rope_scaling_factor <= 1.0:
                 raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""					)









"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowercase_							=							{
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							=							['LayoutLMv2TokenizerFast']

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							=							['LayoutLMv2FeatureExtractor']
		lowercase_							=							['LayoutLMv2ImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							=							[
		    'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'LayoutLMv2ForQuestionAnswering',
		    'LayoutLMv2ForSequenceClassification',
		    'LayoutLMv2ForTokenClassification',
		    'LayoutLMv2Layer',
		    'LayoutLMv2Model',
		    'LayoutLMv2PreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaLayer,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)
else:
		import sys

		lowercase_							=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)